OpenRaiser/Intern-Atlas huggingface dataset:
Latent Compass: Creation by Navigation
Venue: arXiv cs.AI 2020
Year: 2020

In Marius von Senden's Space and Sight, a newly sighted blind patient describes the experience of a corner as lemon-like, because corners "prick" sight like lemons prick the tongue. Prickliness, here, is a dimension in the feature space of sensory experience, an effect of the perceived on the perceiver that arises where the two interact. In the account of the newly sighted, an effect familiar from one interaction translates to a novel context. Perception serves as the vehicle for generalization, in that an effect shared across different experiences produces a concrete abstraction grounded in those experiences. Cezanne and the post-impressionists, fluent in the language of experience translation, realized that the way to paint a concrete form that best reflected reality was to paint not what they saw, but what it was like to see. We envision a future of creation using AI where what it is like to see is replicable, transferrable, manipulable - part of the artist's palette that is both grounded in a particular context, and generalizable beyond it. An active line of research maps human-interpretable features onto directions in GAN latent space. Supervised and self-supervised approaches that search for anticipated directions or use off-the-shelf classifiers to drive image manipulation in embedding space are limited in the variety of features they can uncover. Unsupervised approaches that discover useful new directions show that the space of perceptually meaningful directions is nowhere close to being fully mapped. As this space is broad and full of creative potential, we want tools for direction discovery that capture the richness and generalizability of human perception. Our approach puts creators in the discovery loop during real-time tool use, in order to identify directions that are perceptually meaningful to them, and generate interpretable image translations along those directions.
Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
Venue: arXiv cs.CY 2020
Year: 2020

While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques. We make three contributions. First, we discuss how such approaches will likely be deemed "algorithmic affirmative action," posing serious legal risks of violating equal protection, particularly under the higher education jurisprudence. Such cases have increasingly turned toward anticlassification, demanding "individualized consideration" and barring formal, quantitative weights for race regardless of purpose. This case law is hence fundamentally incompatible with fairness in machine learning. Second, we argue that the government-contracting cases offer an alternative grounding for algorithmic fairness, as these cases permit explicit and quantitative race-based remedies based on historical discrimination by the actor. Third, while limited, this doctrinal approach also guides the future of algorithmic fairness, mandating that adjustments be calibrated to the entity's responsibility for historical discrimination causing present-day disparities. The contractor cases provide a legally viable path for algorithmic fairness under current constitutional doctrine but call for more research at the intersection of algorithmic fairness and causal inference to ensure that bias mitigation is tailored to specific causes and mechanisms of bias.
Six Degree of Freedom Robotic Arm with Mimicking Mechanism
Venue: arXiv cs.RO 2020
Year: 2020

Multi-degree of freedom robots are playing very important role in different applications of automation. They are providing much more accuracy in carrying out a typical procedure as compared to the manual work done by human. In recent years the design, fabrication and development of robotic arms have been active research areas in robotics all around the world. This project describe a mechanical system, design concept and prototype implementation of a 6 DOF robotic arm, which should perform industrial task such as pick and place of fragile objects operation. This robot arm being controlled by micro-controller has base, shoulder, elbow, wrist rotation and a functional gripper. Gripper has been built as end-effector and is capable of grasping diverse objects within own workspace of the arm possible. I made an effort to endeavour one of the best model possible out of the three concepts tried. The three concepts that I undertook were dependent on the type of mechanism used to provide the movement. The first concept was to provide the movement with the help of Pneumatics. The idea of pneumatics did provide a feasible movement but I had to overcome the barrier of the weight as the weight of either the storage of gas or the air compressor would have affected the usage evidently. Similarly, in the second concept, I dealt with the forces of Hydraulics. The forces exerted by hydraulics, not only restricted the movement, but also annexed a certain amount of delay in the time period in which the movement had to be done. In, the third concept, I tried to provide the necessary movement with the help of Servo Motors. The results achieved with servo motors in replacement of pneumatics and hydraulics were impeccable. The motors not only provided the movement with minimal lag but also did not impede the movement of the hand.
BURT: BERT-inspired Universal Representation from Learning Meaningful Segment
Venue: arXiv cs.CL 2020
Year: 2020

Although pre-trained contextualized language models such as BERT achieve significant performance on various downstream tasks, current language representation still only focuses on linguistic objective at a specific granularity, which may not applicable when multiple levels of linguistic units are involved at the same time. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space. We present a universal representation model, BURT (BERT-inspired Universal Representation from learning meaningful segmenT), to encode different levels of linguistic unit into the same vector space. Specifically, we extract and mask meaningful segments based on point-wise mutual information (PMI) to incorporate different granular objectives into the pre-training stage. We conduct experiments on datasets for English and Chinese including the GLUE and CLUE benchmarks, where our model surpasses its baselines and alternatives on a wide range of downstream tasks. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space through a task-independent evaluation. Finally, we verify the effectiveness of our unified pre-training strategy in two real-world text matching scenarios. As a result, our model significantly outperforms existing information retrieval (IR) methods and yields universal representations that can be directly applied to retrieval-based question-answering and natural language generation tasks.
Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics
Venue: arXiv cs.AI 2020
Year: 2020

We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking in the backdrop of autonomous driving. A general neurosymbolic method for online visual sensemaking using answer set programming (ASP) is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework that is generally usable within hybrid architectures for realtime perception and control. We evaluate and demonstrate with community established benchmarks KITTIMOD, MOT-2017, and MOT-2020. As use-case, we focus on the significance of human-centred visual sensemaking -- e.g., involving semantic representation and explainability, question-answering, commonsense interpolation -- in safety-critical autonomous driving situations. The developed neurosymbolic framework is domain-independent, with the case of autonomous driving designed to serve as an exemplar for online visual sensemaking in diverse cognitive interaction settings in the backdrop of select human-centred AI technology design considerations. Keywords: Cognitive Vision, Deep Semantics, Declarative Spatial Reasoning, Knowledge Representation and Reasoning, Commonsense Reasoning, Visual Abduction, Answer Set Programming, Autonomous Driving, Human-Centred Computing and Design, Standardisation in Driving Technology, Spatial Cognition and AI.
Lip-reading with Hierarchical Pyramidal Convolution and Self-Attention
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a novel deep learning architecture to improving word-level lip-reading. On the one hand, we first introduce the multi-scale processing into the spatial feature extraction for lip-reading. Specially, we proposed hierarchical pyramidal convolution (HPConv) to replace the standard convolution in original module, leading to improvements over the model's ability to discover fine-grained lip movements. On the other hand, we merge information in all time steps of the sequence by utilizing self-attention, to make the model pay more attention to the relevant frames. These two advantages are combined together to further enhance the model's classification power. Experiments on the Lip Reading in the Wild (LRW) dataset show that our proposed model has achieved 86.83% accuracy, yielding 1.53% absolute improvement over the current state-of-the-art. We also conducted extensive experiments to better understand the behavior of the proposed model.
Predicting Organizational Cybersecurity Risk: A Deep Learning Approach
Venue: arXiv cs.CR 2020
Year: 2020

Cyberattacks conducted by malicious hackers cause irreparable damage to organizations, governments, and individuals every year. Hackers use exploits found on hacker forums to carry out complex cyberattacks, making exploration of these forums vital. We propose a hacker forum entity recognition framework (HackER) to identify exploits and the entities that the exploits target. HackER then uses a bidirectional long short-term memory model (BiLSTM) to create a predictive model for what companies will be targeted by exploits. The results of the algorithm will be evaluated using a manually labeled gold-standard test dataset, using accuracy, precision, recall, and F1-score as metrics. We choose to compare our model against state of the art classical machine learning and deep learning benchmark models. Results show that our proposed HackER BiLSTM model outperforms all classical machine learning and deep learning models in F1-score (79.71%). These results are statistically significant at 0.05 or lower for all benchmarks except LSTM. The results of preliminary work suggest our model can help key cybersecurity stakeholders (e.g., analysts, researchers, educators) identify what type of business an exploit is targeting.
A Stock Options Metaphor for Content Delivery Networks
Venue: arXiv cs.NI 2020
Year: 2020

The concept of Stock Options is used to address the scarcity of resources, not adequately addressed by the previous tools of our Prediction Mechanism. Using a Predictive Reservation Scheme, network and disk resources are being monitored through well-established techniques (Kernel Regression Estimators) in a given time frame. Next, an Secondary Market mechanism significantly improves the efficiency and robustness of our Predictive Reservation Scheme by allowing the fast exchange of unused (remaining) resources between the Origin Servers (CDN Clients). This exchange can happen, either by implementing socially optimal practices or by allowing automatic electronic auctions at the end of the day or at shorter time intervals. Finally, we further enhance our Prediction Mechanism; Stock Options are obtained and exercised, depending on the lack of resources at the end of day. As a result, Origin Servers may acquire resources (if required) at a normal price. The effectiveness of our mechanism further improves.
Research in Global Software Engineering: A Systematic Snapshot
Venue: arXiv cs.SE 2020
Year: 2020

This paper reports our extended analysis of the recent literature addressing global software engineering (GSE), using a new Systematic Snapshot Mapping (SSM) technique. The primary purpose of this work is to understand what issues are being addressed and how research is being carried out in GSE -- and comparatively, what work is not being conducted. We carried out the analysis in two stages. In the first stage we analyzed 275 papers published between January 2011 and June 2012, and in the second stage we augmented our analysis by considering a further 26 papers (from the 2013 International Conference on Global Software Engineering (ICGSE'13). Our results reveal that, currently, GSE studies are focused on management- and infrastructure-related factors, using principally evaluative research approaches. Most of the studies are conducted at the organizational level, mainly using methods such as interviews, surveys, field studies and case studies. The USA, India and China are major players in GSE, with USA-India collaborations being the most frequently studied, followed by USA-China. While a considerable number of GSE-related studies have been published since January 2011 they are currently quite narrowly focused, on exploratory research and explanatory theories, and the critical research paradigm has been untouched. An absence of formulative research, experimentation and simulation, and a related focus on evaluative approaches, all suggest that existing tools, methods and approaches from related fields are being tested in the GSE context, even though these may not be inherently applicable to the additional scale and complexity of GSE.
Robust Dialogue Utterance Rewriting as Sequence Tagging
Venue: arXiv cs.CL 2020
Year: 2020

The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different domain. We address this robustness issue by proposing a novel sequence-tagging-based model so that the search space is significantly reduced, yet the core of this task is still well covered. As a common issue of most tagging models for text generation, the model's outputs may lack fluency. To alleviate this issue, we inject the loss signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge improvements of our model over the current state-of-the-art systems on domain transfer.
Multi-Principal Assistance Games: Definition and Collegial Mechanisms
Venue: arXiv cs.GT 2020
Year: 2020

We introduce the concept of a multi-principal assistance game (MPAG), and circumvent an obstacle in social choice theory, Gibbard's theorem, by using a sufficiently collegial preference inference mechanism. In an MPAG, a single agent assists N human principals who may have widely different preferences. MPAGs generalize assistance games, also known as cooperative inverse reinforcement learning games. We analyze in particular a generalization of apprenticeship learning in which the humans first perform some work to obtain utility and demonstrate their preferences, and then the robot acts to further maximize the sum of human payoffs. We show in this setting that if the game is sufficiently collegial, i.e. if the humans are responsible for obtaining a sufficient fraction of the rewards through their own actions, then their preferences are straightforwardly revealed through their work. This revelation mechanism is non-dictatorial, does not limit the possible outcomes to two alternatives, and is dominant-strategy incentive-compatible.
Visual Probing and Correction of Object Recognition Models with Interactive user feedback
Venue: arXiv cs.CV 2020
Year: 2020

With the advent of state-of-the-art machine learning and deep learning technologies, several industries are moving towards the field. Applications of such technologies are highly diverse ranging from natural language processing to computer vision. Object recognition is one such area in the computer vision domain. Although proven to perform with high accuracy, there are still areas where such models can be improved. This is in-fact highly important in real-world use cases like autonomous driving or cancer detection, that are highly sensitive and expect such technologies to have almost no uncertainties. In this paper, we attempt to visualise the uncertainties in object recognition models and propose a correction process via user feedback. We further demonstrate our approach on the data provided by the VAST 2020 Mini-Challenge 2.
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning
Venue: arXiv cs.RO 2020
Year: 2020

Trajectory planning is a fundamental task on various autonomous driving platforms, such as social robotics and self-driving cars. Many trajectory planning algorithms use a reference curve based Frenet frame with time to reduce the planning dimension. However, there is a common implicit assumption in classic trajectory planning approaches, which is that the generated trajectory should follow the reference curve continuously. This assumption is not always true in real applications and it might cause some undesired issues in planning. One issue is that the projection of the planned trajectory onto the reference curve maybe discontinuous. Then, some segments on the reference curve are not the image of any part of the planned path. Another issue is that the planned path might self-intersect when following a simple reference curve continuously. The generated trajectories are unnatural and suboptimal ones when these issues happen. In this paper, we firstly demonstrate these issues and then introduce an efficient trajectory generation method which uses a new transformation from the Cartesian frame to Frenet frames. Experimental results on a simulated street scenario demonstrated the effectiveness of the proposed method.
The Communication Complexity of Payment Computation
Venue: arXiv cs.GT 2020
Year: 2020

Let $(f,P)$ be an incentive compatible mechanism where $f$ is the social choice function and $P$ is the payment function. In many important settings, $f$ uniquely determines $P$ (up to a constant) and therefore a common approach is to focus on the design of $f$ and neglect the role of the payment function. Fadel and Segal [JET, 2009] question this approach by taking the lenses of communication complexity: can it be that the communication complexity of an incentive compatible mechanism that implements $f$ (that is, computes both the output and the payments) is much larger than the communication complexity of computing the output? I.e., can it be that $cc_{IC}(f)>>cc(f)$? Fadel and Segal show that for every $f$, $cc_{IC}(f)\leq exp(cc(f))$. They also show that fully computing the incentive compatible mechanism is strictly harder than computing only the output: there exists a social choice function $f$ such that $cc_{IC}(f)=cc(f)+1$. In a follow-up work, Babaioff, Blumrosen, Naor, and Schapira [EC'08] provide a social choice function $f$ such that $cc_{IC}(f)=Θ(n\cdot cc(f))$, where $n$ is the number of players. The question of whether the exponential upper bound of Fadel and Segal is tight remained wide open. In this paper we solve this question by explicitly providing an $f$ such that $cc_{IC}(f)= exp(cc(f))$. In fact, we establish this via two very different proofs. In contrast, we show that if the players are risk-neutral and we can compromise on a randomized truthful-in-expectation implementation (and not on deterministic ex-post implementation) gives that $cc_{TIE}(f)=poly(n,cc(f))$ for every function $f$, as long as the domain of $f$ is single parameter or a convex multi-parameter domain. We also provide efficient algorithms for deterministic computation of payments in several important domains.
Assessing Information Quality in IoT Forensics: Theoretical Framework and Model Implementation
Venue: arXiv cs.CR 2020
Year: 2020

IoT technologies pose serious challenges to digital Forensics. The acquisition of digital evidence is hindered by the number and extreme variety of IoT items, often lacking physical interfaces, connected in unprotected networks, feeding data to uncontrolled cloud services. In this paper we address "Information Quality" in IoT Forensics, taking into account different levels of complexity and included human factors. After drawing a theoretical framework on data quality and information quality, we focus on forensic analysis challenges in IoT environments, providing a use case of evidence collection for investigative purposes. At the end, we propose a formal framework for assessing information quality of IoT devices for Forensics analysis.
A Survey on Segment Routing with Emphasis on Use Cases in Large Provider Networks
Venue: arXiv cs.NI 2020
Year: 2020

Segment routing is heralded as important technology innovation in large provider networks. In the domain of large telecom service providers, segment routing has the potential to be in the same league as MPLS and IPv6 as well as pave a way towards the successful implementation of SDNs. In this regard, this paper is a survey on the existing segment routing work in the community. We begin by describing how segment routing works, inclusive of the various building blocks. The paper describes the segment routing architecture in terms of its positioning vis-a-vis existing networking technologies, as well as the data-plane and the control plane. Label encoding techniques that are central towards implementing segment routing are then discussed. In order to make segment routing relevant to the current domain of providers, we postulate various use cases, their working and issues of interoperability. The paper also reviews the current set of segment routing implementation cases in the industry. Thereafter we position segment routing among the various provider manifestations.
COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography
Venue: arXiv cs.CV 2020
Year: 2020

Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement. In particular, data scarcity is attributed to the privacy and expensive annotation. And data entanglement is due to the high similarity between benign and malignant masses, of which manifolds reside in lower dimensional space with very small margin. To address these two challenges, we propose a deep learning framework, named Contrastive Identifier Network (\textsc{COIN}), which integrates adversarial augmentation and manifold-based contrastive learning. Firstly, we employ adversarial learning to create both on- and off-distribution mass contained ROIs. After that, we propose a novel contrastive loss with a built Signed graph. Finally, the neural network is optimized in a contrastive learning manner, with the purpose of improving the deep model's discriminativity on the extended dataset. In particular, by employing COIN, data samples from the same category are pulled close whereas those with different labels are pushed further in the deep latent space. Moreover, COIN outperforms the state-of-the-art related algorithms for solving breast cancer diagnosis problem by a considerable margin, achieving 93.4\% accuracy and 95.0\% AUC score. The code will release on ***.